Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (229)
  • Open Access

    PROCEEDINGS

    Mesoscopic Modelling and Optimization of Additive-Manufactured Microlattice Materials for Energy Absorption

    Lijun Xiao1,*, Weidong Song1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010981

    Abstract Additively-manufactured microlattice materials have attracted much attention due to their outstanding mechanical properties and energy absorption capacity. Considering the high cost of 3D printing, numerical simulation has become the most common approach for predicting and optimizing the mechanical performance of micro-lattice materials. The current study provides an efficient method to incorporate the printing process induced geometric defects in the lattice models. Numerical simulations are performed to precisely predict the mechanical response of the printed microlattice materials under quasi-static and dynamic loadings. Furthermore, the microlattice structures are graphically represented based on their mesoscopic structural characteristics. Accordingly, More >

  • Open Access

    ARTICLE

    Using Machine Learning to Determine the Efficacy of Socio-Economic Indicators as Predictors for Flood Risk in London

    Grace Gau1, Minerva Singh2,3,*

    Revue Internationale de Géomatique, Vol.33, pp. 427-443, 2024, DOI:10.32604/rig.2024.055752 - 11 October 2024

    Abstract This study examines how socio-economic characteristics predict flood risk in London, England, using machine learning algorithms. The socio-economic variables considered included race, employment, crime and poverty measures. A stacked generalization (SG) model combines random forest (RF), support vector machine (SVM), and XGBoost. Binary classification issues employ RF as the basis model and SVM as the meta-model. In multiclass classification problems, RF and SVM are base models while XGBoost is meta-model. The study utilizes flood risk labels for London areas and census data to train these models. This study found that SVM performs well in binary… More >

  • Open Access

    PROCEEDINGS

    The Effect of Fatigue Loading Frequency on the Fatigue Crack Growth Behavior of a Nickel-Based Superalloy: Experimental Investigation and Modelling

    Yi Shi1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.2, pp. 1-1, 2024, DOI:10.32604/icces.2024.012366

    Abstract The Nickel-based superalloy is wieldy applied in hot components of aero turbine engine due to its superior mechanical property at elevated temperature. However, the working condition of engine hot components are severe and thus the effect of high temperature, oxidation and time-dependent loading on fatigue crack growth behavior should be considered in structure analysis. In this study, first the effect of environment was experimentally investigated. Stand compact tension (CT) specimens under different temperatures and loading frequencies were tests to evaluate the role of temperature and time-dependent effect on fatigue crack growth. Results show that if… More >

  • Open Access

    PROCEEDINGS

    Investigation on Microscopic Properties of Copper Concentrate Particles by Combining Experiments and DEM Modelling

    Zhenyu Zhu1, Ping Zhou1, Xingbang Wan1, Zhuo Chen1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.2, pp. 1-4, 2024, DOI:10.32604/icces.2024.011777

    Abstract 1 General introduction
    The flash smelting is one of the dominant technologies for copper matte production. To meet the increasing demand, the production capability of flash smelting furnace has been increased several times. However, under current conditions, the segregation of concentrate particles becomes an escalating issue, impacting production efficiency and safety [1]. The DEM modelling is a powerful tool for investigating particle behaviors such as contact and collision, but the lack of accurate microscopic properties of copper concentrate particles makes it challenging to conduct reliable DEM simulations [2]. To address this gap, this study employs both… More >

  • Open Access

    ARTICLE

    Enhanced UAV Pursuit-Evasion Using Boids Modelling: A Synergistic Integration of Bird Swarm Intelligence and DRL

    Weiqiang Jin1,#, Xingwu Tian1,#, Bohang Shi1, Biao Zhao1,*, Haibin Duan2, Hao Wu3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3523-3553, 2024, DOI:10.32604/cmc.2024.055125 - 12 September 2024

    Abstract The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective… More >

  • Open Access

    ARTICLE

    Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

    Mohamed Abdel-Mongy1, Mudassir Iqbal2, M. Farag3, Ahmed. M. Yosri1,*, Fahad Alsharari1, Saif Eldeen A. S. Yousef4

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 525-543, 2024, DOI:10.32604/cmes.2024.052505 - 20 August 2024

    Abstract Alkali-activated materials/geopolymer (AAMs), due to their low carbon emission content, have been the focus of recent studies on ecological concrete. In terms of performance, fly ash and slag are preferred materials for precursors for developing a one-part geopolymer. However, determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported. Therefore, in this study, machine learning methods such as artificial neural networks (ANN) and gene expression programming (GEP) models were developed using MATLAB and GeneXprotools, respectively, for the prediction of compressive strength under variable input materials and content… More >

  • Open Access

    EDITORIAL

    Key Issues for Modelling, Operation, Management and Diagnosis of Lithium Batteries: Current States and Prospects

    Bo Yang1,*, Yucun Qian1, Jianzhong Xu2, Yaxing Ren3, Yixuan Chen4

    Energy Engineering, Vol.121, No.8, pp. 2085-2091, 2024, DOI:10.32604/ee.2024.050083 - 19 July 2024

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Enhancing Exam Preparation through Topic Modelling and Key Topic Identification

    Rudraneel Dutta*, Shreya Mohanty

    Journal on Artificial Intelligence, Vol.6, pp. 177-192, 2024, DOI:10.32604/jai.2024.050706 - 19 July 2024

    Abstract Traditionally, exam preparation involves manually analyzing past question papers to identify and prioritize key topics. This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation, Optical Character Recognition (OCR), and Latent Dirichlet Allocation (LDA) for topic modelling. This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers, aiding students in efficient exam preparation. The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify… More >

  • Open Access

    REVIEW

    A Review of Hybrid Cyber Threats Modelling and Detection Using Artificial Intelligence in IIoT

    Yifan Liu1, Shancang Li1,*, Xinheng Wang2, Li Xu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1233-1261, 2024, DOI:10.32604/cmes.2024.046473 - 20 May 2024

    Abstract The Industrial Internet of Things (IIoT) has brought numerous benefits, such as improved efficiency, smart analytics, and increased automation. However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. This work focuses on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which regularisation and Random Forest were More >

  • Open Access

    ARTICLE

    Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

    Wenxiang Dong, H. Vicky Zhao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 139-170, 2024, DOI:10.32604/cmes.2024.047156 - 16 April 2024

    Abstract Understanding and modeling individuals’ behaviors during epidemics is crucial for effective epidemic control. However, existing research ignores the impact of users’ irrationality on decision-making in the epidemic. Meanwhile, existing disease control methods often assume users’ full compliance with measures like mandatory isolation, which does not align with the actual situation. To address these issues, this paper proposes a prospect theory-based framework to model users’ decision-making process in epidemics and analyzes how irrationality affects individuals’ behaviors and epidemic dynamics. According to the analysis results, irrationality tends to prompt conservative behaviors when the infection risk is low More >

Displaying 11-20 on page 2 of 229. Per Page